Uncertainty in Artificial Intelligence
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Identifying Conditional Causal Effects
Jin Tian
Abstract:
This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects between two sets of variables conditioned on some other variables, in time polynomial in the number of variables in the graph. The identifiable conditional causal effects are expressed in terms of the observed joint distribution.
Keywords: null
Pages: 561-568
PS Link:
PDF Link: /papers/04/p561-tian.pdf
BibTex:
@INPROCEEDINGS{Tian04,
AUTHOR = "Jin Tian ",
TITLE = "Identifying Conditional Causal Effects",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "561--568"
}


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